There are many errors in the original author's code, and the original code cannot be run. After modification, it can run correctly.

warning : please use right version of open-cv.

pip uninstall opencv-python

pip install opencv-contrib-python==3.4.2.17

SafeCV

Vision based algorithms for black-box falsification and safety testing of convolutional neural networks

SafeCV is mainly concerned with the falsification of deep, feed-forward convolutional neural networks. The package requires openCV, Keras, numpy and pomegranate. Running the examples requires matplotlib in addition.

Installation with:

pip install SafeCV

As of right now, the package contains two main algorithms:

• DFMCS [Depth First Monte-Carlo Search] - A single monte-carlo based manipulation simulation based on human perception
• MCTS [Monte-Carlo Tree Search] - A monte-carlo tree search method for creating robust adversarial examples

Later, we will include a two-player game formulation for studying MNIST and CIFAR10 networks.

Usage

Each run of DFMCS and MCTS must first initialize parameters:

params_for_run = MCTS_Parameters(image, class, model)

These parameters can be changed to fit the desired performance of the algorithm. Then, the algorithm can be run with:

best_image, sev, prob, statistics = MCTS(params_for_run)

Where:

• best_image is the best adversarial example that was found,
• sev is the L0 Severity of the adversarial example,
• prob is the softmax output corresponding to the best adversarial example,
• statistics is a tuple of different runtime statistics that help illucidate perfomance

Runtime Parameters

Finally, we give a brief documentation of what each of the parameters controls

• model - The Neural Network model to be queried
• ORIGINAL_IMAGE - The unmodified copy of the image (implicitly protected)
• TRUE_CLASS - The expected classification
• manip_method - a method that takes in two variables (pixel value and a constant) and dictates how the input will be manipulated
• VISIT_CONSTANT - Number of manipulations to make per time step
• SIGMA_CONSTANT - Varience to use when formulating the saliency distribution
• X_SHAPE - size of the X dimension of the input
• Y_SHAPE - size of the Y dimension of the input
• predshape - how to reshape the input before feeding it to the network
• kp, des, r - Keypoint values returned from an OpenCV feature detector
• EPSILON - Constant to be fed into the manipulation method
• verbose - Determines if the user wants to see all of the runtime outputs in the console
• preprocess - User defined method to say how an image should be preprocessed (default is to reshape to predshape and return)
• predict - Method for predicting the class and probability of an input
• small_image - if image is less than 50x50
• inflation_constant - When small_image is true, how much should we inflate the input to get a good saliency distribution
• backtracking_constant - How many pixels to remove at each backtracking step